How to transpose a dataset in a csv file?

Question

For example, i would like to transform:

Name,Time,Score
Dan,68,20
Suse,42,40
Tracy,50,38

Into:

Name,Dan,Suse,Tracy
Time,68,         


        

Answer 1

If the whole file contents fits into memory, you can use

import csv
from itertools import izip
a = izip(*csv.reader(open("input.csv", "rb")))
csv.writer(open("output.csv", "wb")).writerows(a)

You can basically think of zip() and izip() as transpose operations:

a = [(1, 2, 3),
     (4, 5, 6),
     (7, 8, 9)]
zip(*a)
# [(1, 4, 7),
#  (2, 5, 8),
#  (3, 6, 9)]

izip() avoids the immediate copying of the data, but will basically do the same.

Answer 2

Read the CSV into pandas data frame, pandas has build in function for transpose which can be invoked as below.

import pandas as pd

csv = pd.read_csv("test.csv", skiprows=1)
# use skiprows if you want to skip headers
df_csv = pd.DataFrame(data=csv)
transposed_csv = df_csv.T
print(transposed_csv)

Answer 3

Transfer from input.csv to output.csv. Pandas can also help.

import pandas as pd
pd.read_csv('input.csv', header=None).T.to_csv('output.csv', header=False, index=False)

Answer 4

Same answer of nosklo (all credits to him), but for python3:

from csv import reader, writer 
with open('source.csv') as f, open('destination.csv', 'w') as fw: 
    writer(fw, delimiter=',').writerows(zip(*reader(f, delimiter=',')))

Answer 5

The simplest way is:

import numpy as np
import pandas as pd

_mat = pd.read_csv("test.csv")
_mat = _mat[_mat.columns[0:3]].values
_t_mat = np.transpose(_mat)

Result:

• Input matrix is : [[1 2 3] [4 5 6]]
• the output is: [[1 4] [2 5] [3 6]]

Answer 6

If lines is the list of your original text than it should be

for i in range(1,len(lines)):
    lines[i] = lines[i].split(',')

new_lines = []
for i in range(len(lines[0])):
    new_lines.append("%s,%s,%s" % (lines[0][i], lines[1][i], lines[2][i]))

or use csv Python module - http://docs.python.org/library/csv.html